The presently described technology relates generally to image processing and display for digital images. More specifically, the presently described technology relates to adaptive image processing and display for digital and computed radiography images.
X-ray imaging has long been an accepted medical diagnostic tool. X-ray imaging systems are commonly used to capture, as examples, thoracic, cervical, spinal, cranial, and abdominal images that often include information necessary for a doctor to make an accurate diagnosis. X-ray imaging systems typically include an x-ray source and an x-ray sensor. When having a thoracic x-ray image taken, for example, a patient stands with his or her chest against the x-ray source at an appropriate height. X-rays produced by the source travel through the patient's chest, and the x-ray sensor then detects the x-ray energy generated by the source and attenuated to various degrees by different parts of the body. An associated control system obtains the detected x-ray energy from the x-ray sensor and prepares a corresponding diagnostic image on a display.
The diagnostic image is typically of inconsistent quality as initially scanned. For example, the raw image of a small or thin patient is typically high contrast or dark compared to the raw image of a large or thick patient, which is typically low contrast or light. Inconsistent image quality makes it difficult for doctors, technicians, or other medical providers to read and interpret. Furthermore, as a result of the inconsistent quality of images, doctors, technicians, and other medical providers may misdiagnose medical conditions, thereby compromising the health and safety of their patients.
The quality of digital images, such as digital radiography (DR) images or computed radiography (CR) images, is typically improved or enhanced by image processing techniques, such as detail enhancement, dynamic range compression and/or management, scatter reduction, decomposition and/or subtraction (dual energy only), and display window determination, for example. Image processing techniques typically include image processing parameters, such as spatial-domain filtering kernel sizes and weighting coefficients, frequency-domain filtering thresholds, log-subtraction parameters (dual energy only), display window-level/center adjustment parameters, and display window-width adjustment parameters.
One technique for improving image quality is to manually select or adjust an appropriate image processing parameter based on an estimation of patient size or thickness by examination and/or measurement of the patient. For example, an operator or technician typically estimates patient size or thickness by visually examining the patient. Alternatively, for example, an operator or technician may estimate patient size or thickness by measuring the patient. More particularly, the operator or technician may measure the patient with a measuring device, such as a ruler or a tape measure, for example. The estimation of patient size or thickness typically include classifications, such as small, medium, or large, for example, wherein each classification corresponds to a pre-determined range of patient sizes or thicknesses. The operator or technician then manually adjusts or selects an appropriate image processing parameter based on the estimation of patient size or thickness.
There are several disadvantages to improving image quality by manually selecting or adjusting an appropriate image processing parameter based on an estimation of patient size or thickness by examination or measurement. First, manually selecting or adjusting an image processing parameter based on an estimation of patient size or thickness, whether by visual examination or measurement, for example, is not accurate. An operator or technician could easily make a mistake, either in estimating patient size or thickness or in selecting or adjusting an appropriate image processing parameter. Additionally, a broad classification, such as small, medium, or large, for example, typically includes a wide range of patient sizes or thicknesses. Consequently, an operator or technician could easily select the same image processing parameter for two patients of vastly different sizes or thicknesses, which would not be appropriate. Furthermore, the anatomy of an individual patient typically varies in size or thickness. Therefore, a single image processing parameter may not be appropriate even for an individual patient.
Second, manually selecting or adjusting an image processing parameter based on an estimation of patient size or thickness, whether by examination or measurement, for example, is not automatic. In order to be profitable, a hospital or clinic must examine a certain number of patients. Manually examining or measuring each patient prior to imaging takes more time, thereby limiting the number of patients that can be imaged in a given time period. Consequently, manually estimating patient sizes or thicknesses and manually selecting or adjusting image processing parameters not only wastes time, but it also is not cost effective.
Another technique for improving image quality is to manually select or adjust an appropriate image processing parameter based on an estimation of patient size or thickness with automatic exposure control (“AEC”). Image acquisition and patient exposure are typically controlled manually. For example, with manual exposure control, an operator or technician sets exposure peak voltage (kVp), current (mA), and duration (msec). The image acquisition and patient exposure end when the time expires.
Alternatively, image acquisition and patient exposure may be controlled automatically. For example, with automatic exposure control, an operator or technician sets the exposure peak voltage (kVp) and current (mA), but the exposure duration (msec) is determined by an AEC device. More particularly, the AEC device detects exposure energy after going through the patient or imaged object. The image acquisition and patient exposure end when the exposure level reaches an appropriate limit.
The exposure duration or time typically varies depending on the patient or object being imaged. For example, thicker patients or objects typically take longer to image than thinner patients or objects. Consequently, an operator or technician typically estimates patient size or thickness based on the exposure duration or time determined with automatic exposure control and then manually selects or adjusts an appropriate image processing parameter, such as small, medium, or large, for example.
There are several disadvantages to improving image quality by manually selecting or adjusting an appropriate image processing parameter based on an estimation of patient size or thickness with AEC. Manually selecting or adjusting an appropriate image processing parameter based on an estimation of patient size or thickness by AEC is not accurate. The locations or positions of the sensing regions of the AEC device are typically fixed within the imaging system. Therefore, if the patient or selected anatomy of the patient is not properly positioned and aligned with the sensing regions of the AEC device, then the exposure duration, as determined by the AEC device, and thus, the corresponding estimate of patient size or thickness may not be accurate. Additionally, the coverage of the AEC device is typically limited. More particularly, the AEC device does not necessarily cover the entire patient or anatomy to be imaged. In other words, the image or scan area is larger than that of the AEC device. Consequently, the exposure duration determined by the AEC device, and thus, the corresponding estimate of patient size may not be accurate.
Additionally, manually selecting or adjusting an image processing parameter based on an estimation of patient size or thickness with AEC may not be automatic. Although the AEC device automatically determines the exposure duration, the operator or technician typically manually estimates the patient size or thickness based on the exposure duration. Additionally, the operator or technician typically manually selects or adjusts the image processing parameter based on the estimation of patient size or thickness. As previously described, in order to be profitable, a hospital or clinic must examine a certain number of patients. Manually estimating the patient size or thickness and manually selecting or adjusting the image processing parameter requires additional time, thereby limiting the number of patients that can be imaged in a given time period. Consequently, manually estimating patient sizes or thicknesses and manually selecting or adjusting image processing parameters not only wastes time, but also increases cost.
Image processing parameters may also be selected or adjusted automatically based on AEC. However, as described above, accuracy and coverage are still of concern, even with automatic selection or adjustment of image processing parameters based on AEC.
Thus, there is a need for improving image quality in an imaging system. More particularly, there is a need for accurately and automatically determining image processing parameters in an imaging system based on properties of the imaged object or patient.